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About This Role
Location
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USA \| Remote
Employment Type
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Full time
Location Type
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Remote
Department
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Engineering
Compensation
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- Estimated Base Salary $152K – $190K • Offers Equity • Offers Bonus • 10% Annual Bonus
This range is determined by work location and additional factors, including job\-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.
Please note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits.
Company Overview
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Deepgram is the leading platform underpinning the emerging trillion\-dollar Voice AI economy, providing real\-time APIs for speech\-to\-text (STT), text\-to\-speech (TTS), and building production\-grade voice agents at scale. More than 200,000 developers and 1,300\+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice\-native foundation models are accessed through cloud APIs or as self\-hosted and on\-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.
Company Operating Rhythm
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At Deepgram, we expect an AI\-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.
Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.
Additionally, we move at the pace of AI. Change is rapid, and you can expect your day\-to\-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9\-to\-5\.
Deepgram is seeking a Senior Technical Program Manager (AI Tooling \& Systems) to drive execution of large\-scale ML infrastructure and AI tooling initiatives. In this role, you'll own the end\-to\-end delivery of programs that span model serving infrastructure, ML pipelines, internal AI tooling, and real\-time inference systems—working closely with our ML engineers, research teams, and product to unlock capability at scale.
You'll thrive here if you enjoy creating clarity around complex ML system tradeoffs, building tools and processes that accelerate model development and deployment, and partnering across research, engineering, and product to align on technical strategy and execution.
What You'll Do
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- Own end\-to\-end delivery of AI infrastructure programs—from model training pipelines and experiment tracking to inference serving and production monitoring
- Define technical architecture, integration patterns, and rollout strategies for new ML systems and tooling (e.g., vector databases, model servers, evaluation frameworks, prompt engineering platforms)
- Serve as connective tissue between ML research, ML engineering, product, and data teams to align on ML system requirements, capability roadmaps, and deployment timelines
- Drive cost and latency optimization for real\-time inference workloads at scale
- Build lightweight internal tools and processes to accelerate ML iteration cycles (experiment tracking, model versioning, A/B testing infrastructure)
- Identify and resolve technical bottlenecks in training pipelines, serving infrastructure, and model evaluation workflows
- Work closely with ML practitioners to translate research breakthroughs into scalable, observable systems
You'll Love This Role If You
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- Are passionate about building ML systems and infrastructure that powers frontier AI applications
- Enjoy optimizing inference cost, latency, and throughput for LLM and multimodal workloads at scale
- Love solving hard problems at the intersection of ML research and production systems (e.g., distillation, quantization, batching strategies)
- Are excited about frontier model serving technologies, vector search, and real\-time ML inference
- Want to directly enable ML researchers and engineers to iterate faster and ship better models
It's Important That You Have
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- 5\+ years of program management or technical leadership in ML infrastructure, ML platforms, or AI tooling (or equivalent)
- Strong technical acumen in ML systems—ideally hands\-on experience as an ML engineer, systems engineer, or ML infrastructure engineer
- Experience coordinating cross\-functional ML programs (e.g., model training evaluation serving monitoring)
- Proven ability to translate ML/research requirements into robust, scalable infrastructure
- Comfortable working in ambiguity and helping teams navigate complex technical tradeoffs (e.g., accuracy vs. latency vs. cost)
- Excellent communication with both technical and non\-technical stakeholders
- Familiarity with high\-growth or startup environments
It Would Be Great If You Had
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- Hands\-on experience with model serving frameworks (vLLM, TensorRT, TorchServe, or similar)
- Experience optimizing LLM or speech/audio model inference (quantization, distillation, KV\-cache optimization, batching strategies)
- Familiarity with ML experiment tracking and versioning tools (MLflow, Weights \& Biases, DVC, or similar)
- Background in feature stores, vector databases, or real\-time ML systems
- Knowledge of cost optimization for GPU/ML workloads on cloud and on\-premise infrastructure
- Experience with multi\-region model serving or edge deployment
- Hands\-on with relevant frameworks (PyTorch, CUDA, Hugging Face, etc.) or cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
Compensation Range: $152K \- $190K
Salary Context
This $152K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Deepgram, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($171K) sits 22% below the category median. Disclosed range: $152K to $190K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Deepgram AI Hiring
Deepgram has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $274K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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